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Modeling The Market Mix Modeling Problem (Media Mix Optimization)
The document discusses market mix modeling (MMM) presented at the 5th Annual Big Data Conference, emphasizing its complexity in channel attribution and marketing spend allocation. It covers various attribution models and highlights the challenges associated with multi-touch attribution, asserting that MMM provides a more nuanced approach to optimizing marketing budgets. The document also outlines different modeling techniques and the importance of data quality in building effective MMM frameworks.
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Modeling The Market Mix Modeling Problem (Media Mix Optimization)
- 1. Market Mix Modeling: SearchFor The Perfect Mix 5th Annual Big Data Conference (2017), Santa Clara Amit Satsangi More on MMM coming soon. Slideshare.net users please share your thoughts, likes with me (below)
- 2. Amit Satsangi https://www.linkedin.com/in/amitsatsangi/ • DataScientist (Consultant) • 10 years: Analytics, IT and Management experience MS Physics MS Comp Sci MBA
- 3. 3 Online channels andRevenue Allocation
- 4. Problem Definition 4 DisplayAds/Paid Search(SEM) Email Television Ads Print Ads PR Events
- 5. 5 Attribution: Assigning $Value to online events
- 6. Assisting Channel (SEO) Originating Channel (SEM) Assisting Channel (Email) ActivityMarketing Spend Day1: User searches for a beauty product, and clicks on Google Ad SEM ($16) Day 5: Organic Search by user, lands on the company website, provides email, abandons cart SEO ($5) Day 6: User receives email and clicks on it, but still does not buy Email ($0.05) Day 7: User sees ad on Facebook, clicks on the ad, and makes a purchase Revenue Generated $100 Facebook Ad ($2.0) Converting Channel (Facebook) The Channel Attribution Problem
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- 8. 8 Attribution Models Media Mix Optimization
- 9. Issues With Multi-touchAttribution Modeling 9 Soccer How should we distribute the prize money of $10 Million amongst these players? • Give 100% to the goalkeeper(First Click Attribution)? • Give 100% to the player who hit the ball in the goal (Last click Attribution)? • Something else??? Does This Sound Totally Absurd? So, should Attribution Modeling
- 10. Let’s Talk AboutSex 10 01 Detector
- 11. 11 Marketing, Promotion &Advertising 101
- 12. Patterns of AdvertisingResponse 12 02 01 Current Effect Carryover Effect 03 Shape Effect. 04 CompetitiveEffects 37% Dynamic Effects05 06 07 Content Effects Media Effects
- 13. Temporal Effects ofAdvertising 13 Modeling Marketing Mix Gerard J Tellis
- 14. 14 Modeling The MarketMix Modeling Problem
- 15. Market Mix Modeling:A Few Techniques 15 Modeling Marketing Mix (Gerard J Tellis) 1. Basic Linear Model A, P, R, Q: Email, Price, Display Ads, SEO, TV Paid Search, Print 2. Multiplicative Model 3. Logit Model Sales = 169 + 2.5*Email + 45*Price + 3.5*TV + …
- 16. 16 Market Mix Modeling: Experimental Results
- 17.
- 18. Results From TheLinear Regression Model 18
- 19. Results From TheLog-linear Multiplicative Model (I) 19
- 20. Results From TheLog-linear Multiplicative Model (II) licative Model 20
- 21. 21 Learning is theeye of the mind…
- 22. • Channel Attributionfails to provide even a minimally acceptable solution to the problem of Media Mix Optimization (marketing budget allocation) • At best Channel Attribution can be used to determine the most popular “channel paths” that the customers take (using Markov Chains) • Using MMM one can go to various levels of model complexity depending on the effects that are desired: current vs. carryover vs. shape etc. (see Tellis) • With MMM the costs of building and keeping the model up-to- date go up with complexity (accuracy) • Any MMM model (even the most basic one) takes time and effort to build – check for Heteroscedasticity, Collinearity, Normality. Auto-correlation etc. Learnings 22
- 23. It is notthe answer that enlightens, but the question. …Eugene Ionesco